Epigenetic prediction of complex traits and mortality in a cohort of individuals with oropharyngeal cancer

Background: DNA methylation (DNAm) variation is an established predictor for several traits. In the context of oropharyngeal cancer (OPC), where 5-year survival is ~ 65%, DNA methylation may act as a prognostic biomarker. We examined the accuracy of DNA methylation biomarkers of 4 complex exposure traits (alcohol consumption, body mass index [BMI], educational attainment and smoking status) in predicting all-cause mortality in people with OPC. Results: DNAm predictors of alcohol consumption, BMI, educational attainment and smoking status were applied to 364 individuals with OPC in the Head and Neck 5000 cohort (HN5000; 19.6% of total OPC cases in the study), followed up for median 3.9 years; inter-quartile range (IQR) 3.3 to 5.2 years (time-to-event—death or censor). The proportion of phenotypic variance explained in each trait was as follows: 16.5% for alcohol consumption, 22.7% for BMI, 0.4% for educational attainment and 51.1% for smoking. We then assessed the relationship between each DNAm predictor and all-cause mortality using Cox proportional-hazard regression analysis. DNAm prediction of smoking was most consistently associated with mortality risk (hazard ratio [HR], 1.38 per standard deviation (SD) increase in smoking DNAm score; 95% confidence interval [CI] 1.04 to 1.83; P 0.025, in a model adjusted for demographic, lifestyle, health and biological variables). Finally, we examined the accuracy of each DNAm predictor of mortality. DNAm predictors explained similar levels of variance in mortality to self-reported phenotypes. Receiver operator characteristic (ROC) curves for the DNAm predictors showed a moderate discrimination of alcohol consumption (area under the curve [AUC] 0.63), BMI (AUC 0.61) and smoking (AUC 0.70) when predicting mortality. The DNAm predictor for education showed poor discrimination (AUC 0.57). Z tests comparing AUCs between self-reported phenotype ROC curves and DNAm score ROC curves did not show evidence for difference between the two (alcohol consumption P 0.41, BMI P 0.62, educational attainment P 0.49, smoking P 0.19). Conclusions: In the context of a clinical cohort of individuals with OPC, DNAm predictors for smoking, alcohol consumption, educational attainment and BMI exhibit similar predictive values for all-cause mortality compared to self-reported data. These findings may have translational utility in prognostic model development, particularly where phenotypic data are not available

The great screen anomaly—a new frontier in product discovery through functional metagenomics

Functional metagenomics, the study of the collective genome of a microbial community by expressing it in a foreign host, is an emerging field in biotechnology. Over the past years, the possibility of novel product discovery through metagenomics has developed rapidly. Thus, metagenomics has been heralded as a promising mining strategy of resources for the biotechnological and pharmaceutical industry. However, in spite of innovative work in the field of functional genomics in recent years, yields from function-based metagenomics studies still fall short of producing significant amounts of new products that are valuable for biotechnological processes. Thus, a new set of strategies is required with respect to fostering gene expression in comparison to the traditional work. These new strategies should address a major issue, that is, how to successfully express a set of unknown genes of unknown origin in a foreign host in high throughput. This article is an opinionating review of functional metagenomic screening of natural microbial communities, with a focus on the optimization of new product discovery. It first summarizes current major bottlenecks in functional metagenomics and then provides an overview of the general metagenomic assessment strategies, with a focus on the challenges that are met in the screening for, and selection of, target genes in metagenomic libraries. To identify possible screening limitations, strategies to achieve optimal gene expression are reviewed, examining the molecular events all the way from the transcription level through to the secretion of the target gene product